The Importance of HEV Fuel Economy and Two Research Gaps Preventing Real World Implementation of Optimal Energy Management

Asher, Zachary D.; Wifvat, Van; Navarro, Anthony; Samuelsen, Scott; Bradley, Thomas H.

doi:10.4271/2017-26-0106

Cited by 16 publications

(7 citation statements)

References 92 publications

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“…Implementation of an Optimal EMS has been recently reviewed [44] and it was determined that a systems-level viewpoint consistent with autonomous vehicle control best captures Optimal EMS implementation. This system includes subsystems for drive cycle prediction (perception), derivation of the Optimal EMS (planning), and implementation of the Optimal EMS in the vehicle.…”

Section: Optimal Energy Management Strategy Simulationsmentioning

confidence: 99%

“…The systems-level viewpoint of the optimally controlled vehicle model with subsystems for perception, planning, and a vehicle plant [44].…”

Section: Figurementioning

confidence: 99%

“…An existing research gap for commercial implementation of an Optimal EMS is the development of any type of prediction for use in an Optimal EMS [44]. From the limited research that does exist, initial results show that incorporation of traffic information improves prediction quality [45,46] and that an artificial neural network may provide the most robust predictions [47].…”

mentioning

confidence: 99%

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Enabling Prediction for Optimal Fuel Economy Vehicle Control

Asher

Tunnell

Baker

et al. 2018

SAE Technical Paper Series

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V ehicle control using prediction based optimal energy management has been demonstrated to achieve better fuel economy resulting in economic, environmental, and societal benefits. However, research focusing on prediction derivation for use in optimal energy management is limited despite the existence of hundreds of optimal energy management research papers published in the last decade. In this work, multiple data sources are used as inputs to derive a prediction for use in optimal energy management. Data sources include previous drive cycle information, current vehicle state, the global positioning system, travel time data, and an advanced driver assistance system (ADAS) that can identify vehicles, signs, and traffic lights. To derive the prediction, the data inputs are used in a nonlinear autoregressive artificial neural network with external inputs (NARX). Two real world drive cycles were developed for analysis in the Denver, Colorado region: a city-focused drive cycle that passes through downtown as well as a highway-focused drive cycle that transitions across multiple interstates. A validated model of a 2010 Toyota Prius in Autonomie is used to determine the vehicle control fuel economy improvements that are possible from the NARX prediction. The optimal energy management control strategy is determined using dynamic programming due to its ease of use and that the solution produced is the globally optimal solution. The control strategies compared include the existing 2010 Toyota Prius control strategy as a baseline, the neural network prediction optimal energy management control strategy, and a 100% accurate prediction optimal energy management control strategy. Results show that inclusion of various sensors and signals enables a significant amount of the fuel economy improvement with respect to 100% accurate prediction. The conclusion is that prediction based optimal energy management enabled fuel economy improvements can be realized with currently available sensors and signals.

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Section: Optimal Energy Management Strategy Simulationsmentioning

confidence: 99%

“…The systems-level viewpoint of the optimally controlled vehicle model with subsystems for perception, planning, and a vehicle plant [44].…”

Section: Figurementioning

confidence: 99%

mentioning

confidence: 99%

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Enabling Prediction for Optimal Fuel Economy Vehicle Control

Asher

Tunnell

Baker

et al. 2018

SAE Technical Paper Series

Self Cite

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“…Details of an Optimal EMS derivation and implementation can be found in numerous articles [25,34,35]. An overall system-level viewpoint of an Optimal EMS implementation developed in previous research [27] is shown in Figure 10. This system consists of three subsystems: perception, planning, and the vehicle plant.…”

Section: Optimal Energy Management System Derivationmentioning

confidence: 99%

“…The ADAS-generated prediction data is used to derive an Optimal EMS which is implemented in a vehicle model to improve FE. The incorporation of ADAS for prediction is novel and has not been explored in prior work [27]. Several ADAS prediction models are developed and discussed in this article, with various computer vision techniques.…”

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confidence: 99%

Towards Improving Vehicle Fuel Economy with ADAS

Tunnell

Asher

Pasricha

et al. 2018

SAE Technical Paper Series

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Modern vehicles have incorporated numerous safety-focused advanced driver-assistance systems (ADAS) in the last decade including smart cruise control and object avoidance. In this article, we aim to go beyond using ADAS for safety and propose to use ADAS technology to enable predictive optimal energy management and improve vehicle fuel economy (FE). We combine ADAS sensor data with a previously developed prediction model, dynamic programming (DP) optimal energy management control, and a validated model of a 2010 Toyota Prius to explore FE. First, a unique ADAS detection scope is defined based on optimal vehicle control prediction aspects demonstrated to be relevant from the literature. Next, during real-world city and highway drive cycles in Denver, Colorado, a camera is used to record video footage of the vehicle environment and define ADAS detection ground truth. Then, various ADAS algorithms are combined, modified, and compared to the ground truth results. Lastly, the impact of four vehicle control strategies on FE is evaluated: (1) the existing vehicle control, (2) actual ADAS detection for prediction and optimal energy management (we consider two variants ADAS1 and ADAS2 for this strategy), (3) ground truth ADAS detection for prediction and optimal energy management, and (4) 100% accurate prediction and optimal energy management. Results show that the defined ADAS scope and algorithms provide close correlation with ADAS ground truth and can enable FE improvements as part of a prediction-based optimal energy management strategy (EMS). Our proposed approach can leverage existing ADAS technology in modern vehicles to realize prediction-based optimal energy management, thus obtaining FE improvements with minor modifications.

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